Modèles de programmation des applications de traitement du signal et de l'image sur cluster parallèle et hétérogène

摘要

Since a decade, computing systems evolved to parallel and heterogeneous architectures. Composed of several nodes connected via a network and including heterogeneous processing units, clusters achieve high performances. To program these architectures, the user must rely on programming models such as MPI, OpenMP or CUDA. However, it is still difficult to conciliate productivity provided by abstracting the architectural specificities, and performances. In this thesis, we exploit the idea that a programming model specific to a particular domain of application can achieve these antagonist goals. In fact, by characterizing a family of application, it is possible to identify high level abstractions to efficiently model them. We propose two models specific to the implementation of signal and image processing applications on heterogeneous clusters. The first model is static. We enrich it with a task migration feature. The second model is dynamic, based on the StarPU runtime. Both models offer firstly a high level of abstraction by modeling image and signal applications as a data flow graph and secondly they efficiently exploit task, data and graph parallelisms. We validate these models with different implementations and comparisons including two real-world applications of images processing on a CPU-GPU cluster.